Empirical Methods in AI
AbstractIn the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.
How to Cite
Walsh, T. (1998). Empirical Methods in AI. AI Magazine, 19(2), 121. https://doi.org/10.1609/aimag.v19i2.1375
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